As the healthcare industry looks for more technology solutions in a post-COVID world, a Toronto-based startup has come out of stealth to launch an AI-driven platform that analyzes hospital data.
“Our main goal over the next six to nine months is really to try and get this product into the hands of as many hospitals as possible.”
Semantic Health was founded in 2019 with the goal of helping hospitals improve their medical coding and auditing processes. In September, the startup raised a $2.7 million USD seed round of funding, led by The Data Collective (also known as DCVC), with participation from Preface Ventures, Liquid 2, RiSC Capital, and Wayfinder VC.
With seed financing secured and two years of product development under its belt, Semantic Health has now launched its medical data platform publicly, hoping to sell its solution to more hospitals in North America.
The platform facilitates real-time access to clinical information that is created about patients when they seek healthcare.
“It’s very, very difficult for hospitals to make sense of all of that data,” Dr. Nicola Sahar, CEO of Semantic Health, told BetaKit. “They rely on very manual and very broken processes, to extract all of the insights from this information. And they’re spending millions of dollars on this process.”
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Semantic Health uses machine learning to automatically analyze unstructured data on patients, extract insights from the data, and ultimately make it easier for hospitals to use this information. Sahar is a University of Toronto medical graduate who has also worked as a natural language processing researcher at the Vector Institute.
Sahar told BetaKit that after working at a number of hospital sites as a medical doctor, he began looking behind the scenes to determine what was happening to the documentation he was creating about patients.
He discovered there were teams of people hired specifically to read over all the clinical data in order to structure it in a better way. Sahar said this process was not only time-consuming but also prone to error.
“It got me thinking that the technology on the machine learning side has been taking off and hitting almost every other industry, but it’s very difficult to make it work in healthcare,” he added, noting that the healthcare sector often relies on outdated, paper-based systems to sort through information.
Sahar left his medical practice to found Semantic Health. The startup’s business model is based on partnering with hospitals and applying its AI platform to a hospital’s existing workflow.
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The company has spent the last year working with a small group of hospitals to test and validate the product remotely. He claimed these initial partnerships generated significant value across multiple metrics, including efficiencies, productivity, data quality, and the number of errors caught in the process.
The startup is already working with a group of hospitals that will be Semantic Health’s next batch of customers. The CEO said Canada and the United States will remain Semantic Health’s primary markets.
Sahar said the pandemic highlighted how critical the problem Semantic Health is looking to solve is for hospitals. He noted that efficiently analyzing data was something hospitals need to do well if they want to treat patients properly, especially during a global health crisis.
“One of the key things that we’ve realized is hospitals really need the product, especially now during the pandemic, when things have been more difficult for them,” Sahar said.
The company currently sits at approximately 10 employees and is hoping to double that number by the end of the year to help bring its product to market.
“Our main goal over the next six to nine months is really to try and get this product into the hands of as many hospitals as possible,” he said.
Image source Unsplash. Photo by Irwan Iwe.
